Search

Abstract For taxonomy based classification of metagenomics assembled contigs, current methods use sequence similarity to identify their most likely taxonomy. However, in the related field of metagenomic binning, contigs are routinely clustered using information from both the contig sequences and their abundance. We introduce Taxometer, a neural network based method that improves the annotations and estimates the quality of any taxonomic classifier using contig abundance profiles and tetra-nucleotide frequencies. We apply Taxometer to five short-read CAMI2 datasets and find that it increases the average share of correct species-level contig annotations of the MMSeqs2 tool from 66.6% to 86.2%. Additionally, it reduce the share of wrong species-level annotations in the CAMI2 Rhizosphere dataset by an average of two-fold for Metabuli, Centrifuge, and Kraken2. Futhermore, we use Taxometer for benchmarking taxonomic classifiers on two complex long-read metagenomics data sets where ground truth is not known. Taxometer is available as open-source software and can enhance any taxonomic annotation of metagenomic contigs.

Abstract Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Objectives To re-evaluate cut-offs for disease activity states according to the Axial Spondyloarthritis Disease Activity Score (ASDAS), and study the impact of sex, age, calendar time, disease and symptom duration on ASDAS and ASDAS cut-offs in a large contemporary cohort.Methods Data from 2939 patients with axial spondyloarthritis (axSpA) starting their first tumour necrosis factor inhibitor in nine European registries were pooled and analysed. Receiver operating characteristic analyses were performed to identify cut-offs against external criteria. Six-month data including patient and physician global assessments, both ≤1 (0–10 integer scale), and Assessment of SpondyloArthritis International Society partial remission were used for separation of inactive disease (ID) from low disease activity (LDA), while patient and physician global ≤3 were applied as external criteria to separate LDA from high disease activity (HDA). Patient and physician global ≥6 were applied to separate HDA from very high disease activity in baseline data.Results The three ASDAS cut-offs identified to separate the four disease activity states in the overall patient population were 3.5. Cut-offs for ID and LDA in women were higher (

Contaminants, such as heavy metals (HMs), accumulate in the Arctic environment and the food web. The diet of the Indigenous Peoples of North Greenland includes locally sourced foods that are central to their nutritional, cultural, and societal health but these foods also contain high concentrations of heavy metals. While bacteria play an essential role in the metabolism of xenobiotics, there are limited studies on the impact of heavy metals on the human gut microbiome, and it is so far unknown if and how Arctic environmental contaminants impact the gut microbes of humans living in and off the Arctic environment. Using a multiomics approach including amplicon, metagenome, and metatranscriptome sequencing, we identified and assembled a near-complete (NC) genome of a mercury-resistant bacterial strain from the human gut microbiome, which expressed genes known to reduce mercury toxicity. At the overall ecological level studied through α- and β-diversity, there was no significant effect of heavy metals on the gut microbiota. Through the assembly of a high number of NC metagenome-assembled genomes (MAGs) of human gut microbes, we observed an almost complete overlap between heavy metal-resistant strains and antibiotic-resistant strains in which resistance genes were all located on the same genetic elements.

Abstract Germline pathogenic variants associated with increased childhood mortality must be subject to natural selection. Here, we analyze publicly available germline genetic metadata from 4,574 children with cancer [11 studies; 1,083 whole exome sequences (WES), 1,950 whole genome sequences (WGS), and 1,541 gene panel] and 141,456 adults [125,748 WES and 15,708 WGS]. We find that pediatric cancer predisposition syndrome (pCPS) genes [n = 85] are highly constrained, harboring only a quarter of the loss-of-function variants that would be expected. This strong indication of selective pressure on pCPS genes is found across multiple lines of germline genomics data from both pediatric and adult cohorts. For six genes [ELP1, GPR161, VHL and SDHA/B/C], a clear lack of mutational constraint calls the pediatric penetrance and/or severity of associated cancers into question. Conversely, out of 23 known pCPS genes associated with biallelic risk, two [9%, DIS3L2 and MSH2] show significant constraint, indicating that they may monoallelically increase childhood cancer risk. In summary, we show that population genetic data provide empirical evidence that heritable childhood cancer leads to natural selection powerful enough to have significantly impacted the present-day gene pool.

Abstract Here we provide a curated, large scale, label free mass spectrometry-based proteomics data set derived from HeLa cell lines for general purpose machine learning and analysis. Data access and filtering is a tedious task, which takes up considerable amounts of time for researchers. Therefore we provide machine based metadata for easy selection and overview along the 7,444 raw files and MaxQuant search output. For convenience, we provide three filtered and aggregated development datasets on the protein groups, peptides and precursors level. Next to providing easy to access training data, we provide a SDRF file annotating each raw file with instrument settings allowing automated reprocessing. We encourage others to enlarge this data set by instrument runs of further HeLa samples from different machine types by providing our workflows and analysis scripts.

Abstract The inflammatory activity in cirrhosis is often pronounced and related to episodes of decompensation. Systemic markers of inflammation may contain prognostic information, and we investigated their possible correlation with admissions and mortality among patients with newly diagnosed liver cirrhosis. We collected plasma samples from 149 patients with newly diagnosed (within the past 6 months) cirrhosis, and registered deaths and hospital admissions within 180 days. Ninety-two inflammatory markers were quantified and correlated with clinical variables, mortality, and admissions. Prediction models were calculated by logistic regression. We compared the disease courses of our cohort with a validation cohort of 86 patients with cirrhosis. Twenty of 92 markers of inflammation correlated significantly with mortality within 180 days (q-values of 0.00–0.044), whereas we found no significant correlations with liver-related admissions. The logistic regression models yielded AUROCs of 0.73 to 0.79 for mortality and 0.61 to 0.73 for liver-related admissions, based on a variety of modalities (clinical variables, inflammatory markers, clinical scores, or combinations thereof). The models performed moderately well in the validation cohort and were better able to predict mortality than liver-related admissions. In conclusion, markers of inflammation can be used to predict 180-day mortality in patients with newly diagnosed cirrhosis. Prediction models for newly diagnosed cirrhotic patients need further validation before implementation in clinical practice. Trial registration: NCT04422223 (and NCT03443934 for the validation cohort), and Scientific Ethics Committee No.: H-19024348.

Abstract Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime.

Abstract Most investigations of geographical within-species differences are limited to focusing on a single species. Here, we investigate global differences for multiple bacterial species using a dataset of 757 metagenomics sewage samples from 101 countries worldwide. The within-species variations were determined by performing genome reconstructions, and the analyses were expanded by gene focused approaches. Applying these methods, we recovered 3353 near complete (NC) metagenome assembled genomes (MAGs) encompassing 1439 different MAG species and found that within-species genomic variation was in 36% of the investigated species (12/33) coherent with regional separation. Additionally, we found that variation of organelle genes correlated less with geography compared to metabolic and membrane genes, suggesting that the global differences of these species are caused by regional environmental selection rather than dissemination limitations. From the combination of the large and globally distributed dataset and in-depth analysis, we present a wide investigation of global within-species phylogeny of sewage bacteria. The global differences found here emphasize the need for worldwide data sets when making global conclusions.

IntroductionPostoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been shown to decrease the associated mortality and morbidity. Combining deep neural networks and genomics with the already established clinical predictors may hold promise for improvement.MethodsThe UK Biobank was utilized to build linear and deep learning models for the prediction of surgery relevant outcomes. An initial GWAS for the relevant outcomes was initially conducted to select the Single Nucleotide Polymorphisms for inclusion in the models. Model performance was assessed with Receiver Operator Characteristics of the Area Under the Curve and optimum precision and recall. Feature importance was assessed with SHapley Additive exPlanations.ResultsModels were generated for atrial fibrillation, venous thromboembolism and pneumonia as genetics only, clinical features only and a combined model. For venous thromboembolism, the ROC-AUCs were 60.1% [59.6%-60.4%], 63.4% [63.2%-63.4%] and 66.6% [66.2%-66.9%] for the linear models and 51.5% [49.4%-53.4%], 63.2% [61.2%-65.0%] and 62.6% [60.7%-64.5%] for the deep learning SNP, clinical and combined models, respectively. For atrial fibrillation, the ROC-AUCs were 60.3% [60.0%-60.4%], 78.7% [78.7%-78.7%] and 80.0% [79.9%-80.0%] for the linear models and 59.4% [58.2%-60.9%], 78.8% [77.8%-79.8%] and 79.8% [78.8%-80.9%] for the deep learning SNP, clinical and combined models, respectively. For pneumonia, the ROC-AUCs were 50.1% [49.6%-50.6%], 69.2% [69.1%-69.2%] and 68.4% [68.0%-68.5%] for the linear models and 51.0% [49.7%-52.4%], 69.7% [.5%-70.8%] and 69.7% [68.6%-70.8%] for the deep learning SNP, clinical and combined models, respectively.ConclusionIn this report we presented linear and deep learning predictive models for surgery relevant outcomes. Overall, predictability was similar between linear and deep learning models and inclusion of genetics seemed to improve accuracy.

Abstract Background Next-generation sequencing (NGS) based population screening holds great promise for disease prevention and earlier diagnosis, but the costs associated with screening millions of humans remain prohibitive. New methods for population genetic testing that lower the costs of NGS without compromising diagnostic power are needed. Methods We developed double batched sequencing where DNA samples are batch-sequenced twice — directly pinpointing individuals with rare variants. We sequenced batches of at-birth blood spot DNA using a commercial 113-gene panel in an explorative (n = 100) and a validation (n = 100) cohort of children who went on to develop pediatric cancers. All results were benchmarked against individual whole genome sequencing data. Results We demonstrated fully replicable detection of cancer-causing germline variants, with positive and negative predictive values of 100% (95% CI, 0.91–1.00 and 95% CI, 0.98–1.00, respectively). Pathogenic and clinically actionable variants were detected in RB1, TP53, BRCA2, APC, and 19 other genes. Analyses of larger batches indicated that our approach is highly scalable, yielding more than 95% cost reduction or less than 3 cents per gene screened for rare disease-causing mutations. We also show that double batched sequencing could cost-effectively prevent childhood cancer deaths through broad genomic testing. Conclusions Our ultracheap genetic diagnostic method, which uses existing sequencing hardware and standard newborn blood spots, should readily open up opportunities for population-wide risk stratification using genetic screening across many fields of clinical genetics and genomics.

Abstract Background Fecal microbiota transplantation (FMT) effectively prevents the recurrence of Clostridioides difficile infection (CDI). Long-term engraftment of donor-specific microbial consortia may occur in the recipient, but potential further transfer to other sites, including the vertical transmission of donor-specific strains to future generations, has not been investigated. Here, we report, for the first time, the cross-generational transmission of specific bacterial strains from an FMT donor to a pregnant patient with CDI and further to her child, born at term, 26 weeks after the FMT treatment. Methods A pregnant woman (gestation week 12 + 5) with CDI was treated with FMT via colonoscopy. She gave vaginal birth at term to a healthy baby. Fecal samples were collected from the feces donor, the mother (before FMT, and 1, 8, 15, 22, 26, and 50 weeks after FMT), and the infant (meconium at birth and 3 and 6 months after birth). Fecal samples were profiled by deep metagenomic sequencing for strain-level analysis. The microbial transfer was monitored using single nucleotide variants in metagenomes and further compared to a collection of metagenomic samples from 651 healthy infants and 58 healthy adults. Results The single FMT procedure led to an uneventful and sustained clinical resolution in the patient, who experienced no further CDI-related symptoms up to 50 weeks after treatment. The gut microbiota of the patient with CDI differed considerably from the healthy donor and was characterized as low in alpha diversity and enriched for several potential pathogens. The FMT successfully normalized the patient’s gut microbiota, likely by donor microbiota transfer and engraftment. Importantly, our analysis revealed that some specific strains were transferred from the donor to the patient and then further to the infant, thus demonstrating cross-generational microbial transfer. Conclusions The evidence for cross-generational strain transfer following FMT provides novel insights into the dynamics and engraftment of bacterial strains from healthy donors. The data suggests FMT treatment of pregnant women as a potential strategy to introduce beneficial strains or even bacterial consortia to infants, i.e., neonatal seeding. Video Abstract

Abstract The many microbial communities around us form interactive and dynamic ecosystems called microbiomes. Though concealed from the naked eye, microbiomes govern and influence macroscopic systems including human health, plant resilience, and biogeochemical cycling. Such feats have attracted interest from the scientific community, which has recently turned to machine learning and deep learning methods to interrogate the microbiome and elucidate the relationships between its composition and function. Here, we provide an overview of how the latest microbiome studies harness the inductive prowess of artificial intelligence methods. We start by highlighting that microbiome data – being compositional, sparse, and high-dimensional – necessitates special treatment. We then introduce traditional and novel methods and discuss their strengths and applications. Finally, we discuss the outlook of machine and deep learning pipelines, focusing on bottlenecks and considerations to address them.

Here, Johansen et al. develop an approach, Phages from Metagenomics Binning (PHAMB), that allows the binning of thousands of viral genomes directly from bulk metagenomics data, while simultaneously enabling clustering of viral genomes into accurate taxonomic viral populations, unveiling viral-microbial host interactions in the gut.

Abstract Background Infections are a major disease burden worldwide. While they are caused by external pathogens, host genetics also plays a part in susceptibility to infections. Past studies have reported diverse associations between human leukocyte antigen (HLA) alleles and infections, but many were limited by small sample sizes and/or focused on only one infection. Methods We performed an immunogenetic association study examining 13 categories of severe infection (bacterial, viral, central nervous system, gastrointestinal, genital, hepatitis, otitis, pregnancy-related, respiratory, sepsis, skin infection, urological and other infections), as well as a phenotype for having any infection, and seven classical HLA loci (HLA-A, B, C, DPB1, DQA1, DQB1 and DRB1). Additionally, we examined associations between infections and specific alleles highlighted in our previous studies of psychiatric disorders and autoimmune disease, as these conditions are known to be linked to infections. Results Associations between HLA loci and infections were generally not strong. Highlighted associations included associations between DQB1*0302 and DQB1*0604 and viral infections (P = 0.002835 and P = 0.014332, respectively), DQB1*0503 and sepsis (P = 0.006053), and DQA1*0301 with “other” infections (a category which includes infections not included in our main categories e.g. protozoan infections) (P = 0.000369). Some HLA alleles implicated in autoimmune diseases showed association with susceptibility to infections, but the latter associations were generally weaker, or with opposite trends (in the case of HLA-C alleles, but not with alleles of HLA class II genes). HLA alleles associated with psychiatric disorders did not show association with susceptibility to infections. Conclusions Our results suggest that classical HLA alleles do not play a large role in the etiology of severe infections. The discordant association trends with autoimmune disease for some alleles could contribute to mechanistic theories of disease etiology.

Background: Atrial Fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, responsible for considerable morbidity and mortality. The heterogenic and complex pathogenesis of AF remains poorly understood, which contributes to the current limitation in effective treatments. We aimed to identify rare genetic variants associated with AF in patients with familial AF.Methods and results: We performed whole exome sequencing in a large family with familial AF and identified a rare variant in the gene CACNA1A c.5053G > A which co-segregated with AF. The gene encodes for the protein variants CaV2.1-V1686M, and is important in neuronal function. Functional characterization of the CACNA1A, using patch-clamp recordings on transiently transfected mammalian cells, revealed a modest loss-of-function of CaV2.1-V1686M.Conclusion: We identified a rare loss-of-function variant associated with AF in a gene previously linked with neuronal function. The results allude to a novel link between dysfunction of an ion channel previously associated with neuronal functions and increased risk of developing AF.

Birch pitch is thought to have been used in prehistoric times as hafting material or antiseptic and tooth imprints suggest that it was chewed. Here, the authors report a 5,700 year-old piece of chewed birch pitch from Denmark from which they successfully recovered a complete ancient human genome and oral microbiome DNA.

Obtaining data on antimicrobial resistance (AMR) from healthy human populations is difficult. Here, Hendriksen et al. use metagenomic analysis to obtain AMR data from untreated sewage from 79 sites in 60 countries, finding correlations with socio-economic, health and environmental factors.

Common genetic variants in structural proteins contribute to risk of atrial fibrillation (AF). Here, using whole-exome sequencing, the authors identify rare truncating variants in TTN that associate with familial and early-onset AF and show defects in cardiac sarcomere assembly in ttn.2-mutant zebrafish.

Summary: Inflammatory signaling is restricted through degradation and the translational repression of cytokine mRNAs. A key factor in this regulation is tristetraprolin (TTP), an RNA-binding protein (RBP) that recruits RNA-destabilizing factors and the translation inhibitory complex 4EHP-GIGYF1/2 to AU-rich element (ARE)-containing mRNAs. Here, we show that the RBP ZNF598 contributes to the same regulatory module in a TTP-like manner. Similar to TTP, ZNF598 harbors three proline-rich motifs that bind the GYF domain of GIGYF1. RNA sequencing experiments showed that ZNF598 is required for the regulation of known TTP targets, including IL-8 and CSF2 mRNA. Furthermore, we demonstrate that ZNF598 binds to IL-8 mRNA, but not TNF mRNA. Collectively, our findings highlight that ZNF598 functions as an RBP that buffers the level of a range of mRNAs. We propose that ZNF598 is a TTP-like factor that can contribute to the regulation of the inflammatory potential of cytokine-producing cells. : Tollenaere et al. highlight a structural and functional resemblance between the ribosome-associated ubiquitin ligase ZNF598 and TTP, the negative regulator of inflammation-associated mRNA stability. Like TTP, ZNF598 contains proline stretches that are bound by GYF domain-containing proteins, binds cytokine mRNAs, and represses inflammatory signaling in resting cells.

For plant-eating insects, we still have only a nascent understanding of the genetic basis of host-use promiscuity. Here, to improve that situation, we investigated host-induced gene expression plasticity in the invasive lobate lac scale insect, Paratachardina pseudolobata (Hemiptera: Keriidae). We were particularly interested in the differential expression of detoxification and effector genes, which are thought to be critical for overcoming a plant's chemical defenses. We collected RNA samples from P. pseudolobata on three different host plant species, assembled transcriptomes de novo, and identified transcripts with significant host-induced gene expression changes. Gene expression plasticity was pervasive, but the expression of most detoxification and effector genes was insensitive to the host environment. Nevertheless, some types of detoxification genes were more differentially expressed than expected by chance. Moreover, we found evidence of a trade-off between expression of genes involved in primary and secondary metabolism; hosts that induced lower expression of genes for detoxification induced higher expression of genes for growth. Our findings are largely consonant with those of several recently published studies of other plant-eating insect species. Thus, across plant-eating insect species, there may be a common set of gene expression changes that enable host-use promiscuity.

Solution hybridization capture methods utilize biotinylated oligonucleotides as baits to enrich homologous sequences from next generation sequencing (NGS) libraries. Coupled with NGS, the method generates kilo to gigabases of high confidence consensus targeted sequence. However, in many experiments, a non-negligible fraction of the resulting sequence reads are not homologous to the bait. We demonstrate that during capture, the bait-hybridized library molecules add additional flanking library sequences iteratively, such that baits limited to targeting relatively short regions (e.g. few hundred nucleotides) can result in enrichment across entire mitochondrial and bacterial genomes. Our findings suggest that some of the off-target sequences derived in capture experiments are non-randomly enriched, and that CapFlank will facilitate targeted enrichment of large contiguous sequences with minimal prior target sequence information.

Our bloodstream is considered to be an environment well separated from the outside world and the digestive tract. According to the standard paradigm large macromolecules consumed with food cannot pass directly to the circulatory system. During digestion proteins and DNA are thought to be degraded into small constituents, amino acids and nucleic acids, respectively, and then absorbed by a complex active process and distributed to various parts of the body through the circulation system. Here, based on the analysis of over 1000 human samples from four independent studies, we report evidence that meal-derived DNA fragments which are large enough to carry complete genes can avoid degradation and through an unknown mechanism enter the human circulation system. In one of the blood samples the relative concentration of plant DNA is higher than the human DNA. The plant DNA concentration shows a surprisingly precise log-normal distribution in the plasma samples while non-plasma (cord blood) control sample was found to be free of plant DNA.

Dendritic cells (DC) play a pivotal regulatory role in activation of both the innate as well as the adaptive immune system by responding to environmental microorganisms. We have previously shown that Lactobacillus acidophilus induces a strong production of the pro-inflammatory and Th1 polarizing cytokine IL-12 in DC, whereas bifidobacteria do not induce IL-12 but inhibit the IL-12 production induced by lactobacilli. In the present study, genome-wide microarrays were used to investigate the gene expression pattern of murine DC stimulated with Lactobacillus acidophilus NCFM and Bifidobacterium bifidum Z9. L. acidophilus NCFM strongly induced expression of interferon (IFN)-beta, other virus defence genes, and cytokine and chemokine genes related to the innate and the adaptive immune response. By contrast, B. bifidum Z9 up-regulated genes encoding cytokines and chemokines related to the innate immune response. Moreover, B. bifidum Z9 inhibited the expression of the Th1-promoting genes induced by L. acidophilus NCFM and had an additive effect on genes of the innate immune response and Th2 skewing genes. The gene encoding Jun dimerization protein 2 (JDP2), a transcription factor regulating the activation of JNK, was one of the few genes only induced by B. bifidum Z9. Neutralization of IFN-beta abrogated L. acidophilus NCFM-induced expression of Th1-skewing genes, and blocking of the JNK pathway completely inhibited the expression of IFN-beta. Our results indicate that B. bifidum Z9 actively inhibits the expression of genes related to the adaptive immune system in murine dendritic cells and that JPD2 via blocking of IFN-beta plays a central role in this regulatory mechanism.

Abstract Emergent brain computed tomography (CT) scan allows for identification of patients presenting with acute severe neurological symptoms in whom medical and surgical interventions may be lifesaving. The aim of this study was to evaluate if time to CT from arrival at the emergency department exceeded 30 min in patients admitted with acute severe neurological symptoms. This was a retrospective register-based quality assurance study. We identified patients admitted to the emergency department with acute severe neurological symptoms between April 1st, 2016 and September 30th, 2020. Data were retrieved from the registry of acute medical team activations. We considered that time to CT from arrival at the emergency department should not exceed 30 min in more than 10% of patients. A total of 559 patients were included. Median time from arrival at the emergency department until CT scan was 24 min (IQR 16–35) in children ( 60 years). This time interval exceeded 30 min for 8.2% (95% CI 6.1–10.9) of all included patients, 35.3% of children, 5.9% of adults, and 8.6% of elders. No children died within 30 days. The 30-day mortality was 21.3% (95% CI 16.4–27) in adults, and 43.9% (95% CI 38.2–49.8) in elders. Time from arrival at our emergency department until brain CT scan exceeded 30 min in 8.2% of all included patients but exceeded the defined quality aim in children and could be improved.